AI/ML System Architect, Silicon - Google
San Diego, CA
About the Job
Minimum qualifications:
- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, with an emphasis on AI/ML hardware and software, a related field, or equivalent practical experience.
- 3 years of experience in AI/ML hardware (AI accelerator, GPU) or software (e.g., Generative AI, Transformer, LLM, Stable Diffusion, Deep Neural Networks, Convolution Neural Networks).
- Experience with coding (e.g., Python, C/C++, TensorFlow, Java, Kotlin, SQL).
Preferred qualifications:
- Master's degree in Electrical Engineering, Computer Engineering, Computer Science, with an emphasis on AI/ML hardware and software, a related field, or equivalent practical experience.
- Experience in Android operating system.
- Experience in low power mobile processors.
- Knowledge of multimedia processing, such as image and audio.
- Knowledge of power and performance management features (e.g., DVFS - Dynamic Voltage and Frequency Scaling, thread scheduling, clock/power gating, QoS, throttling.).
About the job
Google engineers develop the next-generation technologies that change how users connect, explore, and interact with information and one another. As a member of an extraordinarily creative, motivated and talented team, you develop new products that are used by millions of people. We need our engineers to be versatile and passionate to take on new problems as we continue to push technology forward. If you get excited about building new things and working across discipline lines, then our team might be your next career step.
Google's mission is to organize the world's information and make it universally accessible and useful. Our team combines the best of Google AI, Software, and Hardware to create radically helpful experiences. We research, design, and develop new technologies and hardware to make computing faster, seamless, and more powerful. We aim to make people's lives better through technology.
Responsibilities
- Enhance Dynamic Voltage and Frequency Scaling (DVFS) software development for future Generative Artificial Intelligence (GenAI) use cases.
- Implement software prototypes (codes) for AI energy efficiency (e.g., Generative AI, Large Language Model (LLM), Multi-modal, Text-to-Image, and Image Enhancement).
- Explore energy efficient Artificial Intelligence (AI) processing on mobile processors.
- Analyze hardware/software profile data to identify energy/performance inefficiency areas for future AI use cases.